Proposed new signal for real-time stress monitoring: Combination of physiological measures.

Human stress is a physiological tension that appears when a person responds to mental, emotional, or physical chal-lenges. Detecting human stress and developing methods to manage it, has become an important issue nowadays. Auto-matic stress detection through physiological signals may be a useful method to solve this problem. In most of the earlier studies, long-term time window was considered for stress detection. Continuous and a real-time representation of the stress level are usually done through one physiological signal. In this paper, a real-time stress monitoring system is pro-posed which shows the user a new signal for feedback stress level. This signal is the combination of weighted features of galvanic skin response and photoplethysmography signals. The features are defined in 20-sec time windows. Correlation feature selection and linear regression methods are used for feature selection and feature combination, respectively. Furthermore, a set of experiments was conducted to train and test of the proposed model. The proposed model can represent the relative stress level perfectly and has 79% accuracy for classifying the stress and relaxation phases into two categories by a determined threshold.